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Open AccessJournal ArticleDOI

The status and challenge of global fire modelling

TLDR
The Fire Model Intercomparison Project (FireMIP) is an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions as discussed by the authors.
Abstract
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.

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Journal ArticleDOI

Deep learning and process understanding for data-driven Earth system science

TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
Journal ArticleDOI

Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models

TL;DR: Environmental changes that are stressing terrestrial and marine ecosystems are described and how these stressors are being included in ESMs are discussed, initially with an emphasis on climate processes, but also show their emerging utility for VIA analyses and examine them in the context of Earth system prediction.
References
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Journal ArticleDOI

Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model

TL;DR: The LPJ model as mentioned in this paper combines process-based, large-scale representations of terrestrial vegetation dynamics and land-atmosphere carbon and water exchanges in a modular framework, including feedback through canopy conductance between photosynthesis and transpiration and interactive coupling between these 'fast' processes and other ecosystem processes.
Journal ArticleDOI

Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009)

TL;DR: In this paper, the authors used a revised version of the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model and improved satellite-derived estimates of area burned, fire activity, and plant productivity to calculate fire emissions for the 1997-2009 period on a 0.5° spatial resolution with a monthly time step.
Book ChapterDOI

Carbon and Other Biogeochemical Cycles

TL;DR: For base year 2010, anthropogenic activities created ~210 (190 to 230) TgN of reactive nitrogen Nr from N2 as discussed by the authors, which is at least 2 times larger than the rate of natural terrestrial creation of ~58 Tg N (50 to 100 Tg nr yr−1) (Table 6.9, Section 1a).
Book

A Mathematical Model for Predicting Fire Spread in Wildland Fuels

TL;DR: In this paper, a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models.
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